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Comparison of Linear No-Threshold Model and Radiation Hormesis Model for Stochastic Risk Analysis

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Published: 18th May 2020 in Sciences

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Comparison of Linear No-Threshold Model and Radiation Hormesis Model for Stochastic Risk Analysis

Abstract

For more than 60 years, the linear no-threshold model (LNT) has been the accepted theory for radiation protection against stochastic effects. Recent scientific evidence has emerged that counters the LNT theory, instead favoring the radiation hormesis hypothesis. The hormesis model theorizes that low doses of radiation are beneficial, rather than harmful. Extensive literature has been written on case studies that the plausibility of this model. Evidence for the hormesis theory has been shown in data collected from the Hiroshima and Nagasaki nuclear disasters, as well as in the famous case of the ‘Radium Girls’. Additionally, current research is being conducted on the use of whole-body irradiation as a form of cancer treatment. Based on strong scientific support for the hormesis model, the nuclear community should consider revising the current hypothesis used to determine stochastic risk in radiation protection regulations.

Introduction

The linear no-threshold (LNT) model is widely accepted as the basis for determining stochastic risk in current radiation protection standards. It has been in place since its initial approval in 1956 at an international nuclear conference (Vietti-Cook 2015). Under the LNT model, stochastic risk is thought to increase in a linear manner with any radiation dose, meaning that even small amounts of background radiation exposure lead to greater risk. The occurrence of stochastic effects leads to outcomes in the body such as cancer and genomic changes that can be passed on to future offspring  (Hall and Giaccia 2012). In the past few years, strong evidence has been collected that shows the lack of accuracy in the LNT model. Such data instead supports the radiation hormesis model, which follows the basis that low levels of radiation are beneficial, rather than harmful (Doss 2013), (Cuttler and Pollycove 2008).

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There are various cases and ongoing research efforts that demonstrate how the hormesis theory is a more accurate representation of stochastic risk than the LNT model. This is important for society because the overly conservative LNT hypothesis has caused drastic measures to be taken in order to minimize radiation exposure to the public, which has led to increased fear of nuclear science (Ropeik 2016). Mass amounts of spending occur annually to implement radiation protection standards that may not actually be necessary for low levels of radiation exposure, according to the hormesis model (Office of the Chief Financial Officer 2018). Additionally, there may even be potential medical uses for low-dose radiation that utilize the hormesis hypothesis (Oakley 2015). The concept of hormesis in radiation protection has become increasingly plausible and will continue to be proven through further research and investigation.

Discussion

The nuclear attacks in Hiroshima and Nagasaki of 1945 have provided scientists with extremely valuable case studies regarding the stochastic effects of radiation exposure in the bombing survivors. The 1950-2020 Life Span Study examines the health outcomes and cancer occurrences in 86,000 people who were within 2.5 kilometers of the disasters (Doss 2013), (Hall and Giaccia 2012). With the LNT hypothesis as a guideline, scientists predicted that 10-30% of this group would experience fatal radiation-induced cancer within 40 years. Currently, less than 1% of the subjects have died from such cancer (Doss 2013). In this case, the LNT model failed to provide a close estimate of stochastic effects in this cohort of survivors (Cuttler and Pollycove 2008). In fact, those participating in the Life Span Study show a cancer incidence rate below the expected level, meaning that this data follows the trend of radiation hormesis  (Doss 2013).

The well-known case of the ‘Radium Girls’ provides further evidence against the LNT model (Hall and Giaccia 2012). Starting in the 1920s, a large group of female factory workers unknowingly swallowed significant amounts of radium in luminescent paint while licking the tips of paintbrushes used to draw watch dials. Soon, high rates of bone cancer began to occur in these girls, but this was not seen before around 100 microcuries of initial activity (Hall and Giaccia 2012). This case study shows that an exposure threshold was necessary to succeed before any increase in stochastic effects were observed, which does not agree with the concept of the LNT model.

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Typical cancer treatment using radiation therapy involves high doses of radiation delivered to the specific site of a malignant tumor (Oakley 2015). New research is underway that investigates total body irradiation (TBI) as a different approach to cancer therapy. This form of treatment delivers a low radiation dose to the entire body of a cancer patient, allowing the immune system to respond to the radiation and kill the tumor in a more mild manner with less dose (Oakley 2015). Radiation-induced hormesis promotes cell division in the body by increasing the activity of growth factor receptors which activate transcription of the cell (Szumiel 2012). Damage to DNA is more easily repaired and therefore not as prevalent in the body after cell proliferation. This investigation shows promise for the accuracy of the hormesis model in real-world situations (Szumiel 2012). As scientific research developments continue to emerge, there is a growing awareness of the overly conservative nature of the LNT hypothesis. Both the American Nuclear Society and Health Physics Society have acknowledged that there is insufficient evidence supporting the LNT model and that a threshold for stochastic risks exists at about 100 mSv (Cuttler and Pollycove 2008).

The use of the LNT model has led to the ALARA concept in radiation protection standards. The acronym, ‘ALARA’, means “as low as reasonably achievable”. This concept is used as a guideline in the nuclear industry to minimize exposure to radiation to the fullest extent possible (Hall and Giaccia 2012). While safety should always be the priority for nuclear workers and the public, ALARA implementation is not necessary for minimal doses of radiation when following the hormesis hypothesis (Cuttler and Pollycove 2008). In order to meet strict ALARA standards that follow the LNT model, the nuclear sector currently spends a massive amount of money on fees and procedures that ensure compliance with NRC protocol. This can be seen below in Graph 1. In 2019, an estimated $815.4 million will be spent on annual licensing fees owed by NRC-regulated organizations (Office of the Chief Financial Officer 2018). Far too much time and too many resources are being poured into ALARA compliance when, in reality, it is not necessary nor feasible for the nuclear power industry to continue to do so.

Conclusion

 Although the LNT hypothesis has stood as the basis for radiation protection for decades to predict stochastic risk, it has not proven to be accurate when applied to real-world situations. Case studies, including those of the ‘Radium Girls’ and the Hiroshima and Nagasaki survivors, have produced data that is inconsistent with the LNT theory (Doss 2013), (Hall and Giaccia 2012). Current procedures to reduce the amount of radiation exposure to nuclear workers and the public are creating unnecessary fear of radiation in society (Ropeik 2016). Additionally, the nuclear sector is spending excessive amounts of money to enforce ALARA, which is an unnecessary practice according to the hormesis hypothesis (Office of the Chief Financial Officer 2018). Radiation hormesis is a newer model that is currently under investigation but has already been shown to more accurately predict stochastic effects due to radiation than the LNT model. Looking forward, further analysis should be done on the hormesis model by applying the concept to more case studies. Moving away from the conservative LNT theory and towards a more realistic model, such as radiation hormesis, will provide future benefits to the nuclear sector and society.

References

  • Cuttler, J.M., and M. Pollycove. 2008. “Nuclear Energy and Health: And the Benefits of Low-Dose Radiation Hormesis.” Dose-Response. doi:https://doi.org/10.2203/dose-response.08-024.Cuttler.
  • Doll, R, and R. Wakeford. 1997. “Risk of childhood cancer from fetal irradiation.” British Journal of Radiology. doi:10.1259/bjr.70.830.9135438.
  • Doss, Mohan. 2013. “Linear No-Threshold Model vs. Radiation Hormesis.” Dose-Response. doi:https://doi.org/10.2203/dose-response.13-005.Doss.
  • Hall, Eric J., and Amato J. Giaccia. 2012. Radiobiology for the Radiologist. 7th. Philadelphia, PA: Lippincott Williams & Wilkins.
  • Hendee, William R., and Michael K. O’Connor. 2012. “Radiation Risks of Medical Imaging.” Radiology 264 (2).
  • Krishnamoorthi, R., N. Ramarajan, N.E. Wang, B. Newman, E. Rubesova, C.M. Mueller, and R.A. Barth. 2011. “Effectiveness of a Staged US and CT Protocol for the Diagnosis of Pediatric Appendicitis: Reducing Radiation Exposure in the Age of ALARA.” Radiology 259 (1). doi:https://doi-org.ezproxy.proxy.library.oregonstate.edu/10.1148/radiol.10100984.
  • Oakley, Paul A. 2015. “Is Use of Radiation Hormesis the Missing Link to a Better Cancer Treatment?” Journal of Cancer Therapy 6: 601-605. doi:http://dx.doi.org/10.4236/jct.2015.67065.
  • Office of the Chief Financial Officer, U.S. Nuclear Regulatory Commission. 2018. Congressional Budget Justification: Fiscal Year 2019 (NUREG-1100, Volume 34). https://www.nrc.gov/reading-rm/doc-collections/nuregs/staff/sr1100/v34/.
  • Ropeik, David. 2016. “The dangers of radiophobia.” Bulletin of the Atomic Scientists 72 (5): 311-317. doi:10.1080/00963402.2016.1216670.
  • Szumiel, Irena. 2012. “Radiation hormesis: Autophagy and other cellular mechanisms.” International Journal of Radiation Biology 88 (9): 619-628. doi:10.3109/09553002.2012.699698.
  • Vietti-Cook, Annette L. 2015. “10 CFR Part 20, Linear No-Threshold Model and Standards for Protection Against Radiation .” Federal Register, June 23.

 

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